Conventional thermal-image pedestrian detection technology is based on the premise that humanlike thermal images are high-brightness regions, and thus a thermal image is cut by thresholding to obtain several high-brightness possible pedestrian regions, so as to effectuate thermal-image pedestrian detection by humanlike samples or feature comparison. However, the efficiency of the aforesaid algorithm depends on the selection of a threshold. As a result, it does not apply to plenty surroundings, scenes, and weathers. To circumvent the aforesaid issue about the selection of a threshold, the thermal-image pedestrian detection technology nowadays entails describing humanlike profiles by texture features, defining a training database criterion, using plenty of humanlike and non-humanlike samples, training by machine learning a classifier capable of discerning effectively humanlike and non-humanlike samples, and scanning thermal images directly with the classifier, so as to circumvent erroneous cutting-related problems otherwise resulting from nowadays threshold selection.
Although the machine learning-based technology can circumvent cutting-related and cope with problems, such as difference in brightness between clothes in thermal images (cloth distortion) as well as difference in pedestrians' appearance (appearance variation), it fails to effectively overcome un-calibrated white-black polarity changes caused by thermal sensors. Un-calibrated white-black polarity arises from the brightness of humanlike regions in thermal images in contrast with ambient temperature. When the ambient temperature is low (for example, at dusk and at night), thermal image humanlike regions are high-brightness regions. Conversely, when the ambient temperature is high (for example, at noon and in the afternoon), thermal image humanlike regions are low-brightness regions. Hence, the prior art is effectively applicable to a specific situation but not in all weathers (including daytime and nighttime.)